کشف الگوهای مکانی-زمانی سفر در حمل‌ونقل عمومی شهری: بینش‌هایی از تحلیل داده‌های کارت هوشمند

Public transportation systems exhibit intricate travel behaviors influenced by a combination of spatial and temporal dynamics, such as passenger origins, time-based demand, and urban structural features. Deciphering these behaviors is critical for enhancing transportation planning and urban development. This research examines zone-specific travel trends in Mashhad, Iran, utilizing smart card data collected from the city’s bus and metro networks. Temporal patterns in passenger journeys were categorized into morning, midday, and evening periods using K-means clustering across 253 traffic zones. Simultaneously, Mean Shift clustering was employed to investigate spatial characteristics, including population distribution and urban development within each zone. The results reveal distinct clusters for both temporal and spatial dimensions, underscoring the interplay between mobility trends, demographic factors, and land-use configurations. Key findings demonstrate strong associations between residential zones and morning commutes, commercial and educational hubs with midday travel, and the connectivity of peripheral zones with adjacent residential neighborhoods. These outcomes offer actionable insights for urban planners and policymakers to refine transportation systems and land-use policies.